Many systems and methods have been developed to collect and statistically analyze data and data sets. These systems and methods have become increasingly important in complex manufacturing processes, such as those employed by the automotive industry. In the automotive industry, the build of a vehicle can be controlled and monitored by measuring a number of geometric features on each vehicle as they are produced. The geometric feature may include a hole, corner, edge, plane, or similar geometric shape on the vehicle or of a vehicle substructure. Geometric features subject to quantitative dimensional measurement are conventionally named as checkpoints. The checkpoints generate as a result of the geometric measurement real numbers indicating dimensional properties of the individual car bodies or of their subassemblies. These numbers are related to the dimensional variation of tools, robots, etc., in manufacturing process and to the component variability and handling. Sets of these numbers form the process data. Vehicle geometric quality and the manufacturing process stability can be considerably improved and manufacturing costs can be greatly reduced by quickly identifying and fixing problems in the manufacturing process that lead to dimensional variation in the build of the vehicle. Accordingly, many systems measure every vehicle in the manufacturing process to quickly identify trends, and they would stop the manufacturing process if vehicles or subcomponents are being built outside of the vehicle specification. Although inline systems can provide a quick indication that a problem exists, identifying the root cause of the problem has been a time consuming activity of manually run analyses, requiring expert teams with significant process knowledge to intuitively look at the data and identify the root cause of the problem through trial and error approaches.
One tool used by the process knowledgeable people to determine the root cause of the problem is principal component analysis. However, the process knowledgeable person must select the relevant checkpoints manually and identify intuitively patterns that are forming by and between various checkpoints being measured in the process. Although principal component analysis can identify major variation patterns and describe each variable's contribution in quantitative terms, principal component analysis is ineffective in processes with large ensembles of checkpoints, including sets of checkpoints unrelated to the process perturbation to be identified and quantified.
In view of the above, it is apparent that there exists a need for an unbiased and improved analysis method and system, to identify and locate significant process patterns and events in the geometric dimensional variation of the checkpoints.